

import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCamelCase     			=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
_UpperCamelCase     			=     '''    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'''



class        lowercase		(			unittest.TestCase   ):



 '''simple docstring'''


 def     UpperCamelCase__				(self					)					->   Any:






  """simple docstring"""





  UpperCAmelCase__     							=							tempfile.mkdtemp()
  os.makedirs(os.path.join(self.transformer_dir			,			'models/bert/'					)					)
  UpperCAmelCase__     							=							self.transformer_dir
  shutil.copy(
      os.path.join(UpperCamelCase__			,			'src/transformers/models/bert/modeling_bert.py'					)			,			os.path.join(self.transformer_dir			,			'models/bert/modeling_bert.py'					)			,			)




 def     UpperCamelCase__				(self					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__     							=							'src/transformers'
  shutil.rmtree(self.transformer_dir					)




 def     UpperCamelCase__				(self			,			__a			,			__a			,			__a			,			__a=None					)					->   Tuple:






  """simple docstring"""





  UpperCAmelCase__     							=							comment + F"\nclass {class_name}(nn.Module):\n" + class_code
  if overwrite_result is not None:
   UpperCAmelCase__     							=							comment + F"\nclass {class_name}(nn.Module):\n" + overwrite_result
  UpperCAmelCase__     							=							black.Mode(target_versions={black.TargetVersion.PYaa}			,			line_length=119					)
  UpperCAmelCase__     							=							black.format_str(UpperCamelCase__			,			mode=UpperCamelCase__					)
  UpperCAmelCase__     							=							os.path.join(self.transformer_dir			,			'new_code.py'					)
  with open(UpperCamelCase__			,			'w'			,			newline='\n'					) as f:
   f.write(UpperCamelCase__					)
  if overwrite_result is None:
   self.assertTrue(len(check_copies.is_copy_consistent(UpperCamelCase__					)					) == 0					)
  else:
   check_copies.is_copy_consistent(f.name			,			overwrite=UpperCamelCase__					)
   with open(UpperCamelCase__			,			'r'					) as f:
    self.assertTrue(f.read()			,			UpperCamelCase__					)




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__     							=							check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'					)
  self.assertEqual(UpperCamelCase__			,			UpperCamelCase__					)




 def     UpperCamelCase__				(self					)					->   Optional[Any]:






  """simple docstring"""





  self.check_copy_consistency(
      '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'			,			'BertLMPredictionHead'			,			REFERENCE_CODE + '\n'			,			)

  # With no empty line at the end
  self.check_copy_consistency(
      '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'			,			'BertLMPredictionHead'			,			UpperCamelCase__			,			)

  # Copy consistency with rename
  self.check_copy_consistency(
      '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'			,			'TestModelLMPredictionHead'			,			re.sub('Bert'			,			'TestModel'			,			UpperCamelCase__					)			,			)

  # Copy consistency with a really long name
  UpperCAmelCase__     							=							'TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
  self.check_copy_consistency(
      F"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"			,			F"{long_class_name}LMPredictionHead"			,			re.sub('Bert'			,			UpperCamelCase__			,			UpperCamelCase__					)			,			)

  # Copy consistency with overwrite
  self.check_copy_consistency(
      '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'			,			'TestModelLMPredictionHead'			,			UpperCamelCase__			,			overwrite_result=re.sub('Bert'			,			'TestModel'			,			UpperCamelCase__					)			,			)




 def     UpperCamelCase__				(self					)					->   List[str]:






  """simple docstring"""





  UpperCAmelCase__     							=							check_copies.LOCALIZED_READMES['README_zh-hans.md']

  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
      ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
      ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
      ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'
      ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'
      ' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
      ' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'
      ' method has been applied to compress GPT2 into'
      ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
      ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
      ' Multilingual BERT into'
      ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
      ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'
      ' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'
      ' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'
      ' Luong, Quoc V. Le, Christopher D. Manning.'
  )
  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
      ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
      ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
      ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
  )
  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
      ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
      ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
      ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'
      ' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'
      ' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'
      ' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'
      ' method has been applied to compress GPT2 into'
      ' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'
      ' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'
      ' Multilingual BERT into'
      ' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'
      ' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'
      ' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'
      ' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'
      ' Christopher D. Manning 发布。\n'
  )

  UpperCAmelCase__							,       UpperCAmelCase__     							=							check_copies.convert_to_localized_md(
      UpperCamelCase__			,			UpperCamelCase__			,			localized_readme['format_model_list']					)

  self.assertFalse(UpperCamelCase__					)
  self.assertEqual(UpperCamelCase__			,			UpperCamelCase__					)

  UpperCAmelCase__							,       UpperCAmelCase__     							=							check_copies.convert_to_localized_md(
      UpperCamelCase__			,			UpperCamelCase__			,			localized_readme['format_model_list']					)

  # Check whether the number of models is equal to README.md after conversion.
  self.assertTrue(UpperCamelCase__					)

  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'
      ' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'
      ' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'
      ' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'
  )
  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'
      ' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
      ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
      ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
  )
  UpperCAmelCase__     							=							(
      '1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'
      ' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'
      ' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'
      ' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'
  )

  UpperCAmelCase__							,       UpperCAmelCase__     							=							check_copies.convert_to_localized_md(
      UpperCamelCase__			,			UpperCamelCase__			,			localized_readme['format_model_list']					)

  # Check if the model link is synchronized.
  self.assertEqual(UpperCamelCase__			,			UpperCamelCase__					)











'''simple docstring'''







# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


__lowerCAmelCase        =							"pytorch_model.bin"
__lowerCAmelCase        =							"pytorch_model.bin.index.json"
__lowerCAmelCase        =							"adapter_config.json"
__lowerCAmelCase        =							"adapter_model.bin"
__lowerCAmelCase        =							"adapter_model.safetensors"
__lowerCAmelCase        =							"tf_model.h5"
__lowerCAmelCase        =							"tf_model.h5.index.json"
__lowerCAmelCase        =							"model.ckpt"
__lowerCAmelCase        =							"flax_model.msgpack"
__lowerCAmelCase        =							"flax_model.msgpack.index.json"
__lowerCAmelCase        =							"model.safetensors"
__lowerCAmelCase        =							"model.safetensors.index.json"
__lowerCAmelCase        =							"config.json"
__lowerCAmelCase        =							"preprocessor_config.json"
__lowerCAmelCase        =							FEATURE_EXTRACTOR_NAME
__lowerCAmelCase        =							"generation_config.json"
__lowerCAmelCase        =							"modelcard.json"

__lowerCAmelCase        =							"▁"
__lowerCAmelCase        =							SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

__lowerCAmelCase        =							[
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
__lowerCAmelCase        =							[[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
__lowerCAmelCase        =							[[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       if version.parse(lowercase_   ) < version.parse(lowercase_   ):
              if "dev" in min_version:
                     a_						     =			(
                         'This example requires a source install from HuggingFace Transformers (see '
                         '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                     )
              else:
                     a_						     =			F'This example requires a minimum version of {min_version},'
              error_message += F' but the version found is {__version__}.\n'
              raise ImportError(
                  error_message
                  + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                  'versions of HuggingFace Transformers.'   )




